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Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot. nkdnfmlsv sclms ## kicstarter dataset i this data exploration i am uding kick starter data set

kick_data=read.csv("/media/kuldeep/seed2/kicstrter_data/ks-projects-201801.csv")
names(kick_data)
##  [1] "ID"               "name"             "category"        
##  [4] "main_category"    "currency"         "deadline"        
##  [7] "goal"             "launched"         "pledged"         
## [10] "state"            "backers"          "country"         
## [13] "usd.pledged"      "usd_pledged_real" "usd_goal_real"

now we know that what is inside the data we will do univariate visulisation firstl

## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

now we plot the bar graph for knowinkg that which country is making more project using kickstarter now we will find tha

generated

now we will se that whic category is having more success so we will plot a count plot between category vs sucess

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Analysi between fund raised and country

now let us see that how much fund did each startup rise vs country also we will consider that the startup become sucessful,cancelled or fall into some other category

## png 
##   2

as from this we can easily say that those whose goal is too high all of them not definately sucessful there is only one case is usa that a person havin a hogh goal had sucessfully achived it ## US DATA EVALUATION below is given a plot of us data from the whole data of kick starter #1we can ask the question that how much funding is required for a successful project? #2 can also ask question that how much backers are required to get real pledge?

for this we have plotted the graph between the no of backers and usd_pledged_real

us_data=kick_data%>%filter(country=="US")
as=ggplot(data=us_data,aes(x=usd_pledged_real,y=backers,colour=state))+geom_point()+scale_x_continuous(labels = comma)+coord_cartesian(ylim = c(0,10000),xlim = c(0,2500000))

ggplotly(as)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`